{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Isolation Forest outlier detection on KDD Cup '99 dataset\n",
    "\n",
    "## Method\n",
    "\n",
    "[Isolation forests](https://cs.nju.edu.cn/zhouzh/zhouzh.files/publication/icdm08b.pdf) (IF) are tree based models specifically used for outlier detection. The IF isolates observations by randomly selecting a feature and then randomly selecting a split value between the maximum and minimum values of the selected feature. The number of splittings required to isolate a sample is equivalent to the path length from the root node to the terminating node. This path length, averaged over a forest of random trees, is a measure of normality and is used to define an anomaly score. Outliers can typically be isolated quicker, leading to shorter paths.\n",
    "\n",
    "## Dataset\n",
    "\n",
    "The outlier detector needs to detect computer network intrusions using TCP dump data for a local-area network (LAN) simulating a typical U.S. Air Force LAN. A connection is a sequence of TCP packets starting and ending at some well defined times, between which data flows to and from a source IP address to a target IP address under some well defined protocol. Each connection is labeled as either normal, or as an attack.\n",
    "\n",
    "There are 4 types of attacks in the dataset:\n",
    "\n",
    "- DOS: denial-of-service, e.g. syn flood;\n",
    "- R2L: unauthorized access from a remote machine, e.g. guessing password;\n",
    "- U2R: unauthorized access to local superuser (root) privileges;\n",
    "- probing: surveillance and other probing, e.g., port scanning.\n",
    "\n",
    "The dataset contains about 5 million connection records.\n",
    "\n",
    "There are 3 types of features:\n",
    "\n",
    "- basic features of individual connections, e.g. duration of connection\n",
    "- content features within a connection, e.g. number of failed log in attempts\n",
    "- traffic features within a 2 second window, e.g. number of connections to the same host as the current connection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook requires the `seaborn` package for visualization which can be installed via `pip`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install seaborn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import matplotlib\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import os\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from sklearn.metrics import confusion_matrix, f1_score\n",
    "\n",
    "from alibi_detect.od import IForest\n",
    "from alibi_detect.datasets import fetch_kdd\n",
    "from alibi_detect.utils.data import create_outlier_batch\n",
    "from alibi_detect.saving import save_detector, load_detector\n",
    "from alibi_detect.utils.visualize import plot_instance_score, plot_roc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load dataset\n",
    "\n",
    "We only keep a number of continuous (18 out of 41) features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(494021, 18) (494021,)\n"
     ]
    }
   ],
   "source": [
    "kddcup = fetch_kdd(percent10=True)  # only load 10% of the dataset\n",
    "print(kddcup.data.shape, kddcup.target.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Assume that a model is trained on *normal* instances of the dataset (not outliers) and standardization is applied:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(400000, 18) (400000,)\n",
      "0.0% outliers\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(0)\n",
    "normal_batch = create_outlier_batch(kddcup.data, kddcup.target, n_samples=400000, perc_outlier=0)\n",
    "X_train, y_train = normal_batch.data.astype('float'), normal_batch.target\n",
    "print(X_train.shape, y_train.shape)\n",
    "print('{}% outliers'.format(100 * y_train.mean()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "mean, stdev = X_train.mean(axis=0), X_train.std(axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Apply standardization:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "X_train = (X_train - mean) / stdev"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define outlier detector\n",
    "\n",
    "We train an outlier detector from scratch:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No threshold level set. Need to infer threshold using `infer_threshold`.\n"
     ]
    }
   ],
   "source": [
    "filepath = 'my_path'  # change to directory where model is saved\n",
    "detector_name = 'IForest'\n",
    "filepath = os.path.join(filepath, detector_name)\n",
    "\n",
    "# initialize outlier detector\n",
    "od = IForest(threshold=None,  # threshold for outlier score\n",
    "             n_estimators=100)\n",
    "\n",
    "# train\n",
    "od.fit(X_train)\n",
    "\n",
    "# save the trained outlier detector\n",
    "save_detector(od, filepath)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The warning tells us we still need to set the outlier threshold. This can be done with the `infer_threshold` method. We need to pass a batch of instances and specify what percentage of those we consider to be normal via `threshold_perc`. Let's assume we have some data which we know contains around 5% outliers. The percentage of outliers can be set with `perc_outlier` in the `create_outlier_batch` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5.0% outliers\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(0)\n",
    "perc_outlier = 5\n",
    "threshold_batch = create_outlier_batch(kddcup.data, kddcup.target, n_samples=1000, perc_outlier=perc_outlier)\n",
    "X_threshold, y_threshold = threshold_batch.data.astype('float'), threshold_batch.target\n",
    "X_threshold = (X_threshold - mean) / stdev\n",
    "print('{}% outliers'.format(100 * y_threshold.mean()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "New threshold: 0.0797010793476482\n"
     ]
    }
   ],
   "source": [
    "od.infer_threshold(X_threshold, threshold_perc=100-perc_outlier)\n",
    "print('New threshold: {}'.format(od.threshold))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's save the outlier detector with updated threshold:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "save_detector(od, filepath)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Detect outliers\n",
    "\n",
    "We now generate a batch of data with 10% outliers and detect the outliers in the batch. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1000, 18) (1000,)\n",
      "10.0% outliers\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(1)\n",
    "outlier_batch = create_outlier_batch(kddcup.data, kddcup.target, n_samples=1000, perc_outlier=10)\n",
    "X_outlier, y_outlier = outlier_batch.data.astype('float'), outlier_batch.target\n",
    "X_outlier = (X_outlier - mean) / stdev\n",
    "print(X_outlier.shape, y_outlier.shape)\n",
    "print('{}% outliers'.format(100 * y_outlier.mean()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Predict outliers:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "od_preds = od.predict(X_outlier, return_instance_score=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Display results\n",
    "\n",
    "F1 score and confusion matrix:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 score: 0.3279\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels = outlier_batch.target_names\n",
    "y_pred = od_preds['data']['is_outlier']\n",
    "f1 = f1_score(y_outlier, y_pred)\n",
    "print('F1 score: {:.4f}'.format(f1))\n",
    "cm = confusion_matrix(y_outlier, y_pred)\n",
    "df_cm = pd.DataFrame(cm, index=labels, columns=labels)\n",
    "sns.heatmap(df_cm, annot=True, cbar=True, linewidths=.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot instance level outlier scores vs. the outlier threshold:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_instance_score(od_preds, y_outlier, labels, od.threshold)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that the isolation forest does not do a good job at detecting 1 type of outliers with an outlier score around 0. This makes inferring a good threshold without explicit knowledge about the outliers hard. Setting the threshold just below 0 would lead to significantly better detector performance for the outliers in the dataset. This is also reflected by the ROC curve:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "roc_data = {'IF': {'scores': od_preds['data']['instance_score'], 'labels': y_outlier}}\n",
    "plot_roc(roc_data)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
